Abstract Face recognition research in unconstrained environments has focused mainly on the three classical causes of variation, i.e., Pose, Illumination, and Expression (PIE). Another recently identified issue is facial makeup that poses a significant challenge to face recognition systems. We believe that applying makeup can cause changes similar to the ones observed due to illumination variation. Therefore, in this paper, we have investigated the effectiveness of illumination normalization techniques for decreasing the variations caused by makeup in a face recognition system. First, we apply photometric illumination normalization techniques with their parameters adjusted for face recognition. Next, we extract facial features using texture-based feature extraction methods and perform face recognition using Support Vector Machines. Experiments carried out on both constrained and unconstrained databases clearly show that illumination normalization techniques improve face recognition results.

中文翻译：

---

用于化妆不变人脸识别的照明归一化技术

摘要 无约束环境下的人脸识别研究主要集中在三个经典的变化原因上，即姿势、光照和表情（PIE）。最近发现的另一个问题是面部化妆，这对人脸识别系统构成了重大挑战。我们相信化妆会引起类似于由于光照变化而观察到的变化。因此，在本文中，我们研究了照明归一化技术在减少人脸识别系统中化妆引起的变化方面的有效性。首先，我们应用光度照明归一化技术，调整其参数以进行人脸识别。接下来，我们使用基于纹理的特征提取方法提取面部特征，并使用支持向量机进行面部识别。